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THE IMPACT OF SMALL AND MEDIUM ENTERPRISE FINANCING ON THE PROFIT ABILITY OF CONVENTIONAL AND ISLAMIC BANKS IN MALAYSIA
By
AHMAD TARMIZI BIN ABU HANIPAH
Thesis Submitted to
Othman Yeop Abdullah Graduate School of Business, Universiti Utara Malaysia,
in Partial Fulfillment of the Requirement for the Master in Islamic Finance and Banking
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Permission to Use
In presenting this research paper in partial fulfilment of the requirements for a post graduate degree from the Universiti Utara Malaysia (UUM), I agree that the Library of this university may make it freely available for inspection. I further agree that permission for copying of this research paper in any manner, in whole or in part, for scholarly purposes may be granted by my supervisor or, in his absence, by the Dean of Othman Y eop Abdullah Graduate School of Business where I did my research paper. It is understood that any copying or publication or use of this research paper or parts of it for financial gain shall not be allowed without my written permission. It is also understood that due recognition will be given to me and to UUM in any scholarly use which may be made of any material in my research paper.
Request for permission to copy or make other use of the materials in this research paper in whole or in part should be addressed to:
Dean of Othman Yeop Abdullah Graduate School of Business Universiti Utara Malaysia
06010 UUM Sintok Kedah Darul Aman
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Abstract
Small and Medium Enterprises (SMEs) play a very significant role in the economy in terms of balanced and sustainable growth, employment generation, development of entrepreneurial skills and contribution to export earnings. However, despite their importance to the economy, most SMEs are not able to stand up to the challenges of globalization, mainly because of difficulties in securing financing for their development.
The difficulties faced by SMEs in assessing financial support are said to be contributed to their high risk profiles. SMEs are regarded as risky enterprises by the financial institution for a number of reasons. On that score, this study sets out to assess the trend as well as the impact of SME financing, along with other independent variables, on the profitability of 12 Conventional and 12 Islamic banks in Malaysia from the period of 2004 to 2017.
Two dependent variables, Return on Asset (ROA) and Net Interest Margin (NIM) are selected as proxies for bank profitability. All the variables are then regressed using panel data of Random Effects (RE) and Fixed Effects (FE) models with robust standard en-ors.
The regression result shows that while SME financing have a significant but negative impact on Conventional banks' profitability, it has an insignificant and negative impact on Islamic banks' profitability.
Keywords: SME, Conventional banks, Islamic banks, Profitability, ROA, NIM
Abstrak
Perusahaan Kecil and Sederhana (PKS) memainkan peranan yang penting dalam ekonomi di segi pertumbuhan yang mampan dan seimbang, penyediaan peluang pekerjaan, pembangunan kemahiran keusahawanan dan sumbangan dalam pendapatan export. Waiau pun mereka merupakan penyumbang penting kepada kemajuan ekonomi, industri PKS ini tidak berupaya untuk berhadapan dengan cabaran globalisasi disebabkan oleh kesukaran untuk mendapatkan pembiayaan untuk mengembangkan pemiagaan mereka. Kesukaran untuk mendapatkan pembiayaan ini disebabkan oleh profil risiko mercka yang tinggi. Industri PKS dianggap industri yang berisiko tinggi yang menyebabkan institusi kewangan seperti bank agak keberatan untuk memberikan sokongan kewangan kepada industri ini. Justeru itu kajian ini dijalankan untuk mengetahui pola pembiayaan PKS serta kesan pembiayaan PKS, bersama dengan pembolehubah-pembolehubah bebas yang lain, keatas tahap keuntungan 12 bank-bank konvensional dan 12 bank-bank Islam dari tahun 2004 hingga 2017. Dua pembolehubah bebas, Pulangan keatas Asel (ROA) dan Kadar Faedah Bersih/Kadar Untung Bersih (NIM/NPM) dipilih sebagai proksi kepada keuntungan bank. Semua pembolehubah
pembolehubah ini diregresi menggunakan data panel melalui model REM and FEM.
Analisa regresi daripada kajian ini menunjukkan bahawa pembiayaan kepada industri PKS mempunyai kesan yang besar tetapi negatif kepada paras keuntungan bank-bank konvensional manakala bagi bank-bank Islam ia meninggalkan kesan yang kecil tetapi negatif.
Kata Kunci: PKS, Bank konvensional, Bank Islam, Keuntungan, ROA, NIM
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Acknowledgement
In the name of Allah, Most Gracious and Most Merciful. First and foremost, Alhamdulillah, praises to Allah S.W.T for giving me the will and strength to complete this research paper in the quest to fulfill the requirement of Master in Islamic Finance and Banking. I am indebted to many people whose kind assistance has contributed to the successful completion of this research paper.
First, I would like to extend my sincere gratitude to my supervisor, Dr. Mohamad Yazid Isa for his continuous advice, encouragement, and support throughout the writing of this research paper. I really appreciate his knowledge, patience and guidance provided
to
mein the process of completing this study.
My special thanks also to my wife Siti Najad Sahari, my mother Maimunah Abd Razak, my late father Abu Hanifah Zainal Abidin, my daughter Dina 'Alya Hannani and my son Muhammad Danial Luqman for their support, patience and love. To my supportive friends and colleagues for always wishing me success, I extend my sincere thanks.
Last but not least, my sincere gratitude to the lecturers and staff at Universiti Utara Malaysia, Kuala Lumpur and Sintok, for their assistance during my study.
Table of Contents
Permission to Use .ii
Abstrak , .iii
Abstract .iv
Acknowledgement. v
Table of Contents vi
List of Tables ix
List ofFigures x
List of Abbreviations xi
List of Appendices xii
CHAPTER ONE INTRODUCTION l
1 . 1 Background ofStudy l
1.2 Definition of SME 2
1.3 Development of SMEs in Malaysia 3
1.4 Problem Statement .5
1.5 Research Questions 7
1.6 Research Objectives 7
1.7 Significant ofStudy 7
1.8 Scope and Limitation of Study 8
vi
CHAPTER TWO LITERATURE REVIEW 9
2.1 Introduction 9
2.2 Theoretical Review 9
2.2.1 Portfolio Theory 9
2.3 Empirical Review IO
2.3.1 Measure of Bank Profitability 10
2.3.2 Determinants of Conventional Bank's Profitability 1 1
2.3.3 Determinants oflslamic Bank's Profitability 13
2.3.4 Bank and SME Financing 14
CHAPTER 3 DAT A AND METHODOLOGY l 6
3 . 1 Introduction 16
3 .2 Research Design 16
3.3 Research Framework , , . . , , 17
3.4 Model Specification 18
3.5 Panel Regression Models 20
3.6 Variable Description, Measurement of Variables and Hypothesis Development.. .... 2 1
3.7 Econometric Tests 28
CHAPTER 4 FINDINGS AND ANALYSIS 30
4.1 Introduction 30
4.2 Descriptive Analysis 30
4.3 Trend Analysis 33
4.4 Econometric Tests Results 3 7
4.5 Regression Results and Analysis .40
CHAPTER 5 CONCLUSION AND RECOMMENDATION .46
5.1 Introduction , .46
5.2 Summary of Findings .47
5.3 Implication of Findings .48
5.4 Limitation of Study .49
5.5 Recommendations for Future Research 50
5.6 Conclusion 5 1
REFERENCES 53
APPENDICES 67
viii
List of Tables
Table 1 . 1 Category of SME , 3
Table 1.2 Summary ofSME Definition 3
Table 1.3 SME GDP by Economic Sector (2010 constant price) .4 Table 2.1 List of Variables Used as Proxy for Bank Profitability 1 1 Table 3 . 1 List of Sample Islamic and Conventional Banks 17 Table 3.2 List of Dependent and Independent Variables 27 Table 4.1 Descriptive Statistics on Conventional Banks 30
Table 4.2 Descriptive Statistics on Islamic Banks 32
Table 4.3 Coefficient Correlation for Conventional Bank 37
Table 4.4 Coefficient Correlation for Islamic Bank 38
Table 4.5 Variance Inflation Factor for Conventional Banking and Islamic Banking 39 Table 4.6 Regression Results for Return on Asset (ROA) 40 Table 4.7 Regression Results for Net Interest Margin (NIM) .43
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List of Figures
Figure 3.1 Research Framework Model.. 18
Figure 4.1 Trends ofSME Loan/Financing 34
Figure 4.2 Trends on Return on Asset (ROA) 35
Figure 4.3 Trends on Net Interest Margin (NIM) and Net Profit Margin (NPM) 36
x
BLR BNM CB CPI CTA EXPAT FE GDP 18
LSME NPL PAT RE ROA ROE SME TA TL TLTA VIF
List of Abbreviations
Base Lending Rate Bank Negara Malaysia Conventional Banking Consumer Price Index Change in Total Asset
Total Expenses over Profit after Tax Fixed Effects
Gross Domestic Product Islamic Banking
Natural Log of SME Financing Non-Performing Loan
Profit After Tax Random Effects Return on Asset Return on Equity
Small and Medium Enterprise Total Asset
Total Loan
Total Loan over Total Loan Variance Inflation Factor
List of Appendices
Appendix A: Regression Output.. 67
xii
CHAPTER!
INTRODUCTION
1.1 Background of Study
Small and Medium Enterprises (SMEs) have contribute enormously to the economic growth of many countries including Malaysia. In both developed and developing countries, the SME sector plays a fundamental role in promoting economic prosperity through its significant contribution to income creation, absorption of labor and alleviation of poverty. SMEs also contributed to the balanced and sustainable growth, employment generation, development of entrepreneurial skills and contribution to export earnings (Shahchera & Taheri, 2016).
The growth of Small and Medium Enterprise (SME) can be enhanced by having easier access to financing. In the last few decades, a comprehensive financing ecosystem has been put in place to provide diversified funding options for SMEs from both public and private institutions. Financial institutions such as banks provide the bulk of funding to SMEs, accounting for about 97% of total SME lending. Bank lending to SMEs is complemented by Bank Negara Malaysia, other government funding agencies and development financial institutions in the form of schemes and programs. Current financial and non-financial options cover a wide variety of options for every stage of SME business cycle, including for startups and grants for export from public and private institutions.
Despite their important contribution to exports, employment and economic growth, there are many challenges and barriers facing SMEs in Malaysia and for that matter in other countries. One of the major barriers is the access to financing from the
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66
APPENDIX A REGRESSION OUTPUT
CONVENTIONAL BANKING
1. Description
. xtsum roa nim ta exp sme pat tl npl
Variable Mean Std. Dev. Min Max I Observations
- - - - - - - - - - - - - - - - - + - - - - - - - - - - - - - - - - - - - - - + - - - - - - - -
roa overall 1 . 2 2 2 2 7 8 1 :10 0 8 4 -.7 9 1 1 . 1 3 N 158
between . 3 1 5 7 6 8 8 . 8 4 4 6 1 5 4 1 . 7 3 4 2 8 6 n 12
within 1. 0 5 7 8 5 9 - . 4 3 6 0 5 4 9 1 0 . 7 0 8 4 3 T-bar 13 . 1 6 6 7
nim overall 2 . 0 8 7 7 0 7 . 5 6 3 5 5 4 3 . 8 3 7 . 0 5 N 157 between . 3 21 41 6 5 1 . 4 6 7 2 7 3 2.8 2 5 71 4 n 12 within . 4 7 4 3499 1. 2 7 9 5 2 5 6.8 6 9 24 6 T-bar 13 . 0 8 3 3
ta overall 1 . 0 7 e+0 9 l . 0 3 e + 0 8 7 9 4 9 5 9 9 S . l O e + O B N 158
between 9 . 94e + 0 7 3 . 3 4 e + 0 7 3 . 5 9 e + 0 8 n 12
within 4 . 7 3 e+0 7 - 3 . 3 2 e + 0 7 2 . 5 7 e + O B T-bar 1 3 .16 6 7
�
exp overall 1341448 1142746 282823 5880703 N 158
between 11 2 7 7 6 0 4 4 7 9 8 5 . 9 4 2 3 1 2 94 n 12
within 4 8 2 0 3 3 . 3 - 816 9 5 7 . 7 2 9 9 0 8 5 7 T-bar 1 3 . 1 6 6 7
sme overall l . 1 5 e+0 7 l . 3 l e + 0 7 4 0 8 1 2 3 6 . 3 7 e + 0 7 N 158
between l .19 e + 0 7 5 8 8 5 97. 6 3.74e + 0 7 n 12
within 713 299 0 -l .4 5 e + 0 7 4 . 0 4 e + 0 7 T-bar 13 .16 6 7
pat overall 1 2 1 3 2 47 1 2 9 4 8 6 2 -3 31 16 5 6422644 N 158
between 1 1 7 1 6 0 7 3 1 5 8 0 0.4 3 9 9 4 6 6 2 n 12
within 7 1 716 9 . 5 - 3 11 2 5 8 0 3 6 4 1 2 2 9 T-bar 1 3 .16 6 7
tl overall 6 .53 e + 0 7 6 . 0 6 e + 0 7 1 . 3 B e+0 7 3 . 0 0 e + O B N 158
between S . 3 2 e + 0 7 2 .14 e + 0 7 l.7 9 e+0 8 n = 12
within 3 . 5 0 e + 0 7 -B . 4 3 e+0 7 l . 8 6 e+O B T-bar 1 3 . 1 6 6 7
npl overall 2 . 3 0 7 3 4 2 1.9 4 2 9 2 1 . 4 4 1 3.7 5 N 158
between 1 . 0 7 9 0 5 9 . 8 715 3 8 5 4 . 5 6 615 4 n 12
within 1.6 4 4 6 8 4 -. 47 8 812 1 1 1 . 4 9 1 1 9 T-bar 1 3 . 1 6 6 7
67
2 . Correlation
. car eta expat lsme tlta cnpl gdp blr cpi
eta expat lsme tlta cnpl gdp blr cpi
r:
- - - + - - - -- - - -- - - -- - - -- - - -- - - -- - - -- - -- - -
eta 1 . 0 0 0 0
expat - 0 . 0 2 3 4 1 . 0 0 0 0
lsme 0 . 058 2 - 0. 0 8 8 8 1 . 0 0 0 0
tlta -0 .1514 - 0. 0 2 1 8 0 . 0 5 4 1 1 .00 0 0
cnpl -0 . 0 4 3 7 0.0 9 4 0 0 . 0 2 5 1 0 . 2 9 5 6 1 . 0 0 0 0
gdp - 0 . 0 0 2 0 0 . 0 3 40 0 . 0298 0 . 0 5 3 2 0 . 1 6 2 6 1 , 0 0 0 0
blr 0 . 0 0 2 0 0 . 0 9 3 7 0 . 1692 0 . 0 3 3 4 -0 . 0 6 7 6 0 . 5 7 0 6 1 . 0 0 0 0
cpi - 0 . 0 3 9 2 -0 . 0 2 2 4 -0 . 0 1 0 5 0 . 0 1 2 4 - 0 . 1 7 7 8 0 . 3 8 7 4 0 . 5 2 6 4 1 . 0 0 0 0
3 . Regression for ROA Model
. reg roa eta expat lsme tlta cnpl gdp blr cpi
-
Source I SS df MS Number of obs
F ( B , 1 3 7) P r o b> F R-squared Adj R-squared Root MSE
146 117 . 6 9 0.0 0 0 0 0 . 8 7 3 0 0 . 8 6 5 6 . 4 1 2 6 1
-- - - ----+- - - ---- - - ---
Model I 1 6 0 . 2 9 5 0 7 6 8 2 0 . 0 3 6 8 8 4 5 Residual I 2 3 . 3 2 4 1 9 3 4 137 . 1 7 0 2 4 9 5 8 7
- - - +--- - - - - --- - -- - - --- - -
Total I 1 8 3 . 6 1 9 2 6 9 145 1 . 2 6 6 3 3 9 7 9
roa I coef. Std. Err. t P > l t l [95% Conf. Interval]
- - - - -- -- -- - --+- - -- -- - - -- --- - - -- -- - - - --- -- - -
eta -.02 9 2 7 7 3 .0 4 6 3 6 2 - 0.6 3 0 . 5 2 9 -. 1 2 0 9 5 4 9 . 0 6 2 40 0 3 expat - .01 0 4 2 3 1 . 0 0 3 7 3 0 8 - 2 . 7 9 0 . 0 0 6 - . 017 800 4 - . 0 0 3 0 4 5 7 lsme -.07 2 2 3 0 9 . 0 310 4 7 6 - 2 . 3 3 0 . 0 2 1 - . 1 3 3 6 2 5 4 - . 0 1 0 8 3 6 3 tlta 1 . 3 7 2 4 9 3 . 0 4 9 6 2 2 6 2 7 . 6 6 0 . 0 0 0 1 . 2 7 4 3 6 7 1 . 47 0 6 1 8 cnpl . 2 3 4 7 2 9 1 . 0 8 9 6 1 3 6 2 . 6 2 0 . 0 1 0 . 0 5 7 5 2 4 4 . 4 11 9 3 3 8 gdp -. 0 01856 3 . 0 2 1 0 24 2 - 0 . 0 9 0 . 9 3 0 -. 0 4 3 4 3 0 2 . 0 3 9 7 1 7 7 blr . 0 6 4 7 1 0 4 . 13 5 9 7 0 4 0 . 4 8 0 . 6 35 -. 2 0 4 1 6 1 6 . 3 3 3 5 8 2 5 cpi .03 4 711 3 . 0 3 4 5 7 3 1 1. 0 0 0 . 3 1 7 - .03 3 6 5 4 6 . 1 0 3 0 7 7 3
,- cons . 941 6 9 8 3 . 8 3 7 8 3 7 1 1 . 12 0 . 2 6 3 - . 7 1 5 0 6 6 9 2 . 5 9 8 4 6 3
-
�
4 . Variation Inflation Factor.
-
. vif Variable VIF 1/VIF- - - --- - - +- - - -- - -
blr 1 . 92 0 . 5 1 9 9 0 3
gdp 1 . 64 0 . 6 0 8 4 7 0
cpi 1 . so 0 . 6 6 7 6 7 2
cnpl 1 . 2 4 0 . 8 0 3 310
tlta 1 . 1 3 0 . 8 8 2 6 7 2
lsme 1 . 07 0. 9 3 2 7 9 7 expat 1 . 05 0. 9 5 6 9 2 9
eta 1 . 03 0 . 9 7 0 1 40
- --- - - - +- - - --- - -
Mean VIF
I
1 . 3 2. xttestO
Breusch and Pagan Lagrangian multiplier test for random effects r o a(c o d e , t ] = Xb + u[codeJ + e [ c o d e ,t )
Estimated results:
Var sd = sqrt(Var)
- - - + - - - - - - - - - - - - - -
roa e I
u I
1 . 2 6 6 3 4 .1 0 1 2 9 9 6 . 0 9 2 9 7 7 2
1 . 1 2 5 3 1 8 .3 1 8 2 7 6 . 3 0 4 9 2 1 6
. -
- .
Test: Var(u) 0
chibar2 ( o 1) P r o b > chibar2
69
1 1 7 . 2 2 0.0 0 0 0
5 . Hausman Test . hausrnan fe
- --- C o e f f i c i e n t s - - - - (bl
fe
(Bl lb-Bl
Difference
sqrt(diag(V_b-V_B))
S . E .
- - - +- - - -- - - -- -- - - -- -- - - -- - - -- - -
eta - . 019 7 647 -. 0 1 9 3 8 8 2 - . 0 0 0 3 7 6 5 . 0 0 4 7 1 5 8
expat - . 0 0 4 6 1 1 9 - . 0 0 5 0 3 8 9 . 0 0 0 4 2 6 9 . 0 0 0 3 6 7 2 lsme -. 1 8 5 4 5 8 9 - .1 2 6 2 5 31 - . 0 5 9 2 0 5 8 . 0 5 0 938 8 tlta 1 . 4 3 916 2 1. 4 3 2 6 2 2 . 0 0 6 5 3 98 . 0 0 5 3 9 7 4 cnpl .1 9 1 0 1 6 7 . 1 8 7 6 1 8 5 . 0 0 3 3 9 8 2 . 0 10 3 9 6 6 gdp - . 0 0 2 40 4 5 -. 0 0 0 5 1 4 9 - . 0 0 1 8 8 9 6 . 0 0 2 4 0 0 6
cpi . 0 2 4 4 4 3 3 . 0 2 9 6 9 3 6 - . 0 0 5 2 5 0 4 . 0 0 5 12 8 2
blr . 1 1 9 8 7 7 8 . 0 7 5 8 2 1 7 . 0 4 4 0 5 6 1 . 0 4 1 6 2 5 5
b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic
ch i 2 ( 8 )
Prob>chi2
( b-B ) ' I (V_b-V_B) A ( - 1 ) ) (b-B)
4 .0 8 0. 8 4 9 5
6 . Heteroscedacity Test . xttest3
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
chi2 (1 2 ) 1 6 9 9 .4 6
Prob>chi2 0 . 0 0 0 0
7 . Autocorrelation Test
. xtserial eta expat lsme tlta cnpl gdp cpi blr
Wooldridge test for autocorrelation in panel data H O : no first-order autocorrelation
F ( 1 , 1 1 ) P r o b > F
2 3 9 9 . 5 4 9
0 . 0 0 0 0
8. Regression for ROA model with robust standard errors . xtreg roa eta expat lsme tlta cnpl gdp cpi blr , re robust
R-sq: within between overall
corr(u_i, X)
0 . 9 2 4 2 0 . 3 8 3 3 0 . 8 6 7 7
O (assumed)
�umber of obs 146
Number of groups 12
Obs per group: min 10
avg 1 2 . 2
max 13
Wald chi2 ( 8 ) 2 2 4 7 . 5 7 Prob > chi2 0 . 0 0 0 0 ( S t d . Err. adjusted for 12 clusters in code) Random-effects GLS regression
Group variable: code
I Robust
roa I Coef. Std. Err. z P > lzl ( 9 5 % Conf. Interval]
- -- - - -- - - -- - - + -- -- - - --- - -
eta - . 0 1 9 3 8 8 2 . 0 1 8 5 9 3 5 - 1 . 0 4 0 . 2 9 7 - . 0 5 5 8 3 0 7 . 0 1 7 0 5 4 3 expat - . 0 0 5 0 3 8 9 . 0 0 1 5 7 2 2 - 3 . 21 0. 0 0 1 - . 0 0 8 1 2 0 2 - . 0 0 1 9 5 7 5
.
�
lsme - . 1 2 6 2 5 3 1 . 0 6 2 8 1 4 1 -2 . 0 1 0 . 0 4 4 -. 2 4 9 3 6 6 4 - . 0 0 3 1 3 9 8 tlta 1 . 4 3 2 6 2 2 .1 6 9 4 1 5 6 8 . 4 6 0 . 0 0 0 1 . 1 0 0 5 7 3 1 . 7 6 4 6 7 cnpl . 1 8 7 6 1 8 5 .1 3 5 2 2 4 5 1 . 3 9 0 . 165 - . 0 7 7 4 1 6 7 . 4 5 2 6 5 3 7 gdp -. 0 0 0 5 1 4 9 . 0 2 1 3 4 6 9 - 0 . 0 2 0 . 9 8 1 - . 0 4 2 3 5 4 1 . 0 4 1 3 2 4 3 cpi . 0 2 9 6 9 3 6 . 0 1 4 8 5 2 1 2 . 0 0 0 . 0 4 6 . 0 0 0 5 8 4 2 . 0 5 8 8 0 3 1 blr . 0 7 5 8 2 1 7 . 1 2 1 4 9 6 2 0 . 6 2 0 . 5 3 3 - . 1 6 2 3 0 6 5 . 3 1 3 9 4 9 9 - cons 1 . 6 7 4 3 7 2 1 . 1 8 7 6 1 1 . 4 1 0 . 1 5 9 - . 6 5 3 3 0 1 4 . 0 0 2 0 4 5- - - + - - - --- - - --- - - -- - -
sigma_u sigma_e rho
. 3 0 4 9 2 1 6 3 . 3 1 8 2 7 5 9 8
. 4 7 8 5 8 1 0 7 (fraction of variance due to u i )
71
- .
9 . Regression for NIM Model
. reg nim e t a expat lsme tlta cnpl gdp b l r cpi
Source I SS df MS Number of obs
F ( B , 1 3 7 ) P r o b > F R-squared Adj R-squared Root MSB
146 4.0 6 0 .0 0 0 2 0 . 1 9 1 7 0 . 1 4 4 5 . 5 3 9 2
- - - + - - - -- - -
Model I 9 . 4 4 7 9 4 4 2 9 s 1 . 1 8 0 9 9 3 0 4 Residual I 3 9 . 8 3 1 2 5 6 4 137 . 2 9 0 7 3 9 0 9 7
- - - + - - -
Total ] 4 9 .2 7 9 2 0 0 6 145 . 3 3 9 8 5 6 5 5 6
- -- - - -- - - -- - ---- ---- -- -- --- - - - -- -- -- -- -- - - ---- -- -- - - -- - - -- - - --- - -
nim I Coef. Std. Err. t P > l t l [ 9 5 % Conf. Interval]
- - -- - - - -- - - --+ - - - ---- - - -- - ---- - -- - -- -- - -- - - -- - -- - -
eta . 0 0 3 4 6 6 6 . 0 6 0 5 8 5 8 0 .0 6 0. 9 5 4 - . 1 1 6 3 3 7 5 . 1 2 3 2 7 0 8 expat . 0 0 0 5 5 5 3 . 0 0 4 8 7 5 4 0 . 11 0. 9 0 9 - . 0 0 9 0 8 5 5 . 0 1 0 1 9 6 1 lsme -.1 7 1 4 1 3 1 . 0 4 0 5 7 3 - 4 . 2 2 0.0 0 0 -. 2 5 1 6 4 3 4 - . 0 9 1 1 8 2 8 tlta - . 0 0 7 0 2 0 2 . 0 6 4 8 4 6 8 - 0 . 11 0. 9 1 4 - . 1 3 5 2 5 0 2 . 1 2 1 2 0 9 8 cnpl . 0 6 3 0 0 7 5 . 11 7 1 0 6 9 0 . 5 4 0.5 9 1 - . 1 6 8 5 6 3 4 . 2 9 4 5 7 8 3
gdp .0 4 2 3 8 3 7 . 0 2 7 4 7 4 4 1 . 5 4 0.1 2 5 -.0 1 1 9 4 5 . 0 9 6 7 1 2 5
blr -. 4 6 4 0 3 9 8 . 1 7 7 6 8 5 9 -2 . 6 1 0 . 0 1 0 -.8 1 5 4 0 1 4 - . 1 1 2 6 7 8 1 cpi . 0 3 0 0 6 8 3 . 0 4 5 1 8 0 1 0 . 6 7 0 . 5 0 7 -. 0 5 9 2 7 2 1 . 1 1 9 4 0 8 8 cons 7 . 5 1 5 4 1 1 1 . 0 9 4 8 8 4 6 . 8 6 0 . 0 0 0 5 . 3 5 0 3 5 3 9.6 8 0 4 7
. xttestO
Breusch and Pagan Lagrangian multiplier test for random effects
nim {code, t] Xb + u[code] + e [ c o d e , t ]
Estimated results:
Var sd = sqrt(Var)
- - - + - - - -- - -
nim e I
u I
.3 3 9 8 5 6 6
. 2 3 4 2 7 9 9 . 1 0 6 5 8 9 2
. 5 8 2 9 7 2 2 . 4 8 4 0 2 4 7 . 3 2 6 4 8 0 1
T e s t : Var(u) 0
chibar2 ( o 1) P r o b > chibar2
2 6 . 1 8 0 .0 0 0 0 ·
.
-
1 0. H a u s m a n Test . hausman fe- - - - C o e f f i c i e n t s - -- - (bl
fe
(Bl (b-BI
Difference
sqrt(diag(V_b-V_BJ) S . E .
- - - + - - - -- - -
eta -.0 1 9 8 0 5 1 -. 0 1 4 7 8 2 5 -. 0 0 5 0 2 2 6 . 0 1 1 6 2 9 6 expat . 0 0 2 1 0 8 8 . 0 0 1 9 3 5 8 . 0 0 0 1 7 3 . 0 0 0 9 2 2 5 lsme - . 2 3 6 3 2 7 2 -. 1 9 1 3 0 3 - . 0 4 5 0 2 4 2 . 0 9 0 5 1 2 6 tlta -.0 0 2 9 0 7 3 -.0 0 3 1 7 4 . 0 0 0 2 6 6 7 . 0 1 3 2 1 3 9
cnpl .0 2 1 0 2 7 1 . 0 2 2 4 0 9 7 - . 0 0 1 3 8 2 5 . 0 2 3 5 7 9 1
gdp .0 4 2 8 5 0 2 . 0 4 4 1 0 1 3 -. 0 0 1 2 5 1 1 . 0 0 5 3 7 7 2
cpi .01 6 6 7 1 3 . 0 2 1 9 4 6 8 - . 0 0 5 2 7 5 6 . 0 1 0 5 9 0 4
blr - . 4 2 0 9 4 3 5 -. 4 5 5 8 3 3 1 . 0 3 4 8 8 9 6 . 0 7 6 8 9 5
b = consistent under Ho and Ha; obtained from xtreg B inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
c h i 2 ( 8 )
Prob>chi2
lb -B l ' [ (V_b-V_B) • ( - 1 ) ] lb-Bl 0. 5 7
0 . 9 9 9 8
1 1. Heteroscedacity Test . xttest3
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
H O : s i g m a ( i ) A 2 = sigmaA2 for all i chi2 ( 1 2 )
Prob:>chi2
15 1 9 . 7 6 0.0 0 0 0
73
1 2 . Autocorrelation Test
. xtserial eta expat lsme tlta cnpl gdp cpi blr
Wooldridge test for autocorrelation in panel data
H O : no first-order autocorrelation
F ( 1 , 11)
P ro b > F
2 3 9 9 . 5 4 9 0 . 0 0 0 0
1 3. Regression for NIM model with robust standard errors xtreg nim eta expat lsme tlta cnpl gdp cpi b l r , re robust
R-sq: within between overall
corr(u_i, X)
0 . 1 3 79 0 . 2 9 9 8 0 . 1 8 9 5
0 (assumed)
Number of obs 146
Number of groups 12
Obs per group: min 10
avg 12 . 2
max 13
Wald c h i 2 ( 8 ) 1 3 5 . 9 7
Prob > chi2 0 . 0 0 0 0 ( S t d . Err. adjusted for 12 clusters in code) Random-effects GLS regression
Group variable: code
I Robust
nim I Coef. Std. Err. z P > l z l ( 9 5 % Conf. Interval]
- - - -- - - +- - - - -- - - -- - - -- - - --- -- - - -- -- - - - -- - - -- - -
eta -. 0 1 4 7 8 2 5 . 013 4 6 6 9 -1 . 1 0 0 . 2 7 2 - . 0411772 . 0116121 expat . 0 0 1 9 3 5 8 . 0 0 0 7 0 8 1 2 . 7 3 0 . 0 0 6 .0 0 0 5 4 7 9 . 0 0 3 3 2 3 7 lsme -. 1 9 1 3 0 3 . 0 9 1 0 3 9 6 - 2 . 10 0. 0 3 6 -. 3 6 9 7 3 7 4 -.012 8 6 8 5 tlta - .0 0 3 1 7 4 . 0 2 9 5 5 4 1 - 0 . 1 1 0.9 1 4 -. 0 6 1 0 9 8 9 . 0 5 4 7 5 0 9 cnpl . 0 2 2 40 9 7 . 1 14 2 4 9 2 0 . 2 0 0 . 8 4 4 -. 2 0151 4 6 . 2 4 6 3 3 3 9 gdp . 0 4 4 101 3 . 0 5 0 3 8 0 9 0 . 8 8 0 . 3 8 1 -. 0 5 46 4 34 . 1 4 2 84 61 cpi . 0 2 1 9 4 6 8 . 0 1 8 9 9 4 4 1 . 16 0. 2 4 8 -. 0 1 5 2 81 5 . 0 5 9 1 7 5 2 blr -. 4 5 5 8 3 31 . 3 4 9 8 043 - 1 . 30 0 . 1 9 3 -1 . 1 4 1 4 3 7 . 2 2 9 7 7 0 7
·cons 7 . 7 7 9 5 1 8 1 . 2 9 8 3 31 5 . 9 9 0 . 0 0 0 5 . 2 3 4 8 3 7 10 . 3 2 4 2
- - - --- - - +- - - -- - - - --- - - -- - - --- - - --- - - - -- - - - -- - - -- - - -- - -
sigma u I
sigma-e I rho I
.3 2 64 8 0 0 9 .4 9 4 0 2 4 6 5
. 31 2 6 9 9 4 6 (fraction of variance due to u_i)
